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        Package&nbsp;peach
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<!-- ==================== PACKAGE DESCRIPTION ==================== -->
<h1 class="epydoc">Package peach</h1><p class="nomargin-top"><span class="codelink"><a href="peach-pysrc.html">source&nbsp;code</a></span></p>
<p><em>Peach</em> is a pure-Python package with aims to implement techniques of machine
learning and computational intelligence. It contains packages for</p>
<ul class="rst-simple">
<li>Neural Networks, including, but not limited to, multi-layer perceptrons and
self-organizing maps;</li>
<li>Fuzzy logic and fuzzy inference systems, including Mamdani-type and
Sugeno-type controllers;</li>
<li>Optimization packages, including multidimensional optimization;</li>
<li>Stochastic Optimizations, including genetic algorithms, simulated annealing,
particle swarm optimization;</li>
<li>A lot more.</li>
</ul>

<hr />
<div class="fields">      <p><strong>Author:</strong>
        Jos&#233; Alexandre Nalon
      </p>
      <p><strong>Version:</strong>
        0.1.0
      </p>
</div><!-- ==================== SUBMODULES ==================== -->
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  <ul class="nomargin">
    <li> <strong class="uidlink"><a href="peach.fuzzy-module.html">peach.fuzzy</a></strong>: <em class="summary">This package implements fuzzy logic. Consult:</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.fuzzy.base-module.html">peach.fuzzy.base</a></strong>: <em class="summary">This package implements basic definitions for fuzzy logic</em>    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.cmeans-module.html">peach.fuzzy.cmeans</a></strong>: <em class="summary">Fuzzy C-Means</em>    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.control-module.html">peach.fuzzy.control</a></strong>: <em class="summary">This package implements fuzzy controllers, of fuzzy inference systems.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.defuzzy-module.html">peach.fuzzy.defuzzy</a></strong>: <em class="summary">This package implements defuzzification methods for use with fuzzy controllers.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf-module.html">peach.fuzzy.mf</a></strong>: <em class="summary">Membership functions</em>    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.norms-module.html">peach.fuzzy.norms</a></strong>: <em class="summary">This package implements operations of fuzzy logic.</em>    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.ga-module.html">peach.ga</a></strong>: <em class="summary">This package implements genetic algorithms. Consult:</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.ga.base-module.html">peach.ga.base</a></strong>: <em class="summary">Basic Genetic Algorithm (GA)</em>    </li>
    <li> <strong class="uidlink"><a href="peach.ga.chromosome-module.html">peach.ga.chromosome</a></strong>: <em class="summary">Basic definitions and classes for manipulating chromosomes</em>    </li>
    <li> <strong class="uidlink"><a href="peach.ga.crossover-module.html">peach.ga.crossover</a></strong>: <em class="summary">Basic definitions for crossover operations and base classes.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.ga.fitness-module.html">peach.ga.fitness</a></strong>: <em class="summary">Basic definitions and base classes for definition of fitness functions for use
with genetic algorithms.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.ga.mutation-module.html">peach.ga.mutation</a></strong>: <em class="summary">Basic definitions and classes for operating mutation on chromosomes.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.ga.selection-module.html">peach.ga.selection</a></strong>: <em class="summary">Basic classes and definitions for selection operator.</em>    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn-module.html">peach.nn</a></strong>: <em class="summary">This package implements support for neural networks. Consult:</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.nn.af-module.html">peach.nn.af</a></strong>: <em class="summary">Base activation functions and base class</em>    </li>
    <li> <strong class="uidlink"><a href="peach.nn.base-module.html">peach.nn.base</a></strong>: <em class="summary">Basic definitions for layers of neurons.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.nn.kmeans-module.html">peach.nn.kmeans</a></strong>: <em class="summary">K-Means clustering algorithm</em>    </li>
    <li> <strong class="uidlink"><a href="peach.nn.lrules-module.html">peach.nn.lrules</a></strong>: <em class="summary">Learning rules for neural networks and base classes for custom learning.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.nn.mem-module.html">peach.nn.mem</a></strong>: <em class="summary">Associative memories and Hopfield network model.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.nn.nnet-module.html">peach.nn.nnet</a></strong>: <em class="summary">Basic topologies of neural networks.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.nn.rbfn-module.html">peach.nn.rbfn</a></strong>: <em class="summary">Radial Basis Function Networks</em>    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.optm-module.html">peach.optm</a></strong>: <em class="summary">This package implements deterministic optimization methods. Consult:</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.optm.base-module.html">peach.optm.base</a></strong>: <em class="summary">Basic definitons and base class for optimizers</em>    </li>
    <li> <strong class="uidlink"><a href="peach.optm.linear-module.html">peach.optm.linear</a></strong>: <em class="summary">This package implements basic one variable only optimizers.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.optm.multivar-module.html">peach.optm.multivar</a></strong>: <em class="summary">This package implements basic multivariable optimizers, including gradient and
Newton searches.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.optm.quasinewton-module.html">peach.optm.quasinewton</a></strong>: <em class="summary">This package implements basic quasi-Newton optimizers. Newton optimizer is very
efficient, except that inverse matrices need to be calculated at each
convergence step. These methods try to estimate the hessian inverse iteratively,
thus increasing performance.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.optm.stochastic-module.html">peach.optm.stochastic</a></strong>    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.pso-module.html">peach.pso</a></strong>: <em class="summary">Basic Particle Swarm Optimization (PSO)</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.pso.acc-module.html">peach.pso.acc</a></strong>: <em class="summary">Functions to update the velocity (ie, accelerate) of the particles in a swarm.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.pso.base-module.html">peach.pso.base</a></strong>: <em class="summary">This package implements the simple continuous version of the particle swarm
optimizer. In this implementation, it is possible to specify, besides the
objective function and the first estimates, the ranges of search, which will
influence the max velocity of the particles, and the population size. Other
parameters are available too, please refer to the rest of this documentation for
further details.</em>    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.sa-module.html">peach.sa</a></strong>: <em class="summary">This package implements optimization by simulated annealing. Consult:</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.sa.base-module.html">peach.sa.base</a></strong>: <em class="summary">This package implements two versions of simulated annealing optimization. One
works with numeric data, and the other with a codified bit string. This last
method can be used in discrete optimization problems.</em>    </li>
    <li> <strong class="uidlink"><a href="peach.sa.neighbor-module.html">peach.sa.neighbor</a></strong>: <em class="summary">This module implements a general class to compute neighbors for continuous and
binary simulated annealing algorithms. The continuous neighbor functions return
an array with a neighbor of a given estimate; the binary neighbor functions
return a <tt class="rst-rst-docutils literal rst-docutils literal">bitarray</tt> object.</em>    </li>
    </ul>
    </li>
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        <a href="peach-module.html#__doc__" class="summary-name" onclick="show_private();">__doc__</a> = <code title="&quot;&quot;&quot;
*Peach* is a pure-Python package with aims to implement techniques of \
machine
learning and computational intelligence. It contains packages for

- Neural Networks, including, but not limited to, multi-layer perceptr\
ons and
  self-organizing maps;
..."><code class="variable-ellipsis">...</code></code>
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        <a name="__package__"></a><span class="summary-name">__package__</span> = <code title="None">None</code><br />
      hash(x)
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<!-- ==================== VARIABLES DETAILS ==================== -->
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  <h3 class="epydoc">__doc__</h3>
  
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    <dt>Value:</dt>
      <dd><table><tr><td><pre class="variable">
&quot;&quot;&quot;
*Peach* is a pure-Python package with aims to implement techniques of <span class="variable-linewrap"><img src="crarr.png" alt="\" /></span>
machine
learning and computational intelligence. It contains packages for

- Neural Networks, including, but not limited to, multi-layer perceptr<span class="variable-linewrap"><img src="crarr.png" alt="\" /></span>
ons and
  self-organizing maps;
<code class="variable-ellipsis">...</code>
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